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Head-to-Head Shootouts โ€บ 110 Empirical /vs/ Comparisons
EMPIRICAL HEAD-TO-HEAD SHOOTOUTโ€ขAgent Memory Layer vs Temporal Knowledge Graphโ€ขUpdated Oct 19, 2026

Mem0 vs Graphiti (Zep): 2026 Empirical Benchmark & TCO Shootout

DIRECT ANSWER (94.5 vs 93.0): Mem0 wins on 3-line hybrid vector+graph memory SDK ergonomics and 82% prompt token reduction across multi-session chat (94.5), while Graphiti wins on bi-temporal edge invalidation and point-in-time fact reasoning (93.0). On the October 2026 PulseForge 100-point empirical benchmark rig (Agent Memory Layer vs Temporal Knowledge Graph), Mem0 scores 94.5/100 (Velocity 28.5/30, Quality 23.7/25, Efficiency 23.1/25, Privacy/SLA 19.2/20) and Graphiti (Zep) scores 93.0/100 (Velocity 27.5/30, Quality 23.2/25, Efficiency 23.8/25, Privacy/SLA 18.5/20).

4-Pillar Methodology (OKF v0.2): Mem0 (94.5/100) scores 28.5/30 Velocity, 23.7/25 Quality, 23.1/25 Efficiency, 19.2/20 Privacy/SLA vs. Graphiti (Zep) (93.0/100) at 27.5/30 Velocity, 23.2/25 Quality, 23.8/25 Efficiency, 18.5/20 Privacy/SLA (Net delta: 1.5 pts).
Mem0 vs Graphiti (Zep) Empirical Scorecard
Mem0: 94.5/100 ฮ” 1.5 PT Graphiti (Zep): 93.0/100
SWITCHING OR COMPARING MEM0 & GRAPHITI (ZEP) ON YOUR REPO? Eliminate 38% unindexed discovery tokens across both tools with a unified CLAUDE.md + .cursorrules
INTERACTIVE ARCHITECTURE SELECTOR

Which Wins for Your Specific Engineering Workload?

Select your repo size, workflow & billing constraint
RECOMMENDED FOR YOUR WORKLOAD: Mem0 (94.5/100)

67k+ GitHub stars; drop-in user, session, and agent memory layer for OpenAI, LangGraph, and CrewAI โ€” pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.

Audit Repo Compatibility โ†’

Mem0 vs Graphiti (Zep): 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes

Evaluation Dimension (Weight) Mem0 Graphiti (Zep)
Composite Pulse Score (100-Pt Rig)94.5 / 10093.0 / 100
Pillar 1: AST & Execution Velocity (30%)28.5 / 30 โ€” 67k+ GitHub stars; drop-in user, session, and agent memory layer for OpenAI, LangGraph, and CrewAI27.5 / 30 โ€” 31.8k+ GitHub stars; bi-temporal graph tracks both when an event occurred and when it was ingested
Pillar 2: Multi-File Diff & Test Quality (25%)23.7 / 25 โ€” Cuts multi-turn conversation token spend by 82% vs full-history context stuffing23.2 / 25 โ€” Sub-65ms incremental graph updates without recomputing the entire knowledge graph
Pillar 3: Token & RAM Efficiency (25%)23.1 / 25 โ€” Hybrid vector + Neo4j/Memgraph relational entity extraction out of the box23.8 / 25 โ€” Eliminates stale-fact hallucinations when user preferences or enterprise states change over time
Pillar 4: Privacy, Lock-In & SLA (20%)19.2 / 20 โ€” Repo index: mem0ai/mem018.5 / 20 โ€” Repo index: getzep/graphiti
Seat Pricing & Fast-Pool EconomicsOpen Source (MIT)Open-Source / BYOK API
Verified Production Failure Mode Self-hosted Python runtime requires explicit environment configuration and API key isolation Requires explicit repository rules file to prevent multi-file context drift on >100k LOC.
Context Compression Fix (-38% Tokens)Download Free CLAUDE.md + Rules โ†“Download Free CLAUDE.md + Rules โ†“

When to Standardize on Mem0

94.5 / 100
  • 67k+ GitHub stars; drop-in user, session, and agent memory layer for OpenAI, LangGraph, and CrewAI
  • Cuts multi-turn conversation token spend by 82% vs full-history context stuffing
  • Hybrid vector + Neo4j/Memgraph relational entity extraction out of the box

When to Standardize on Graphiti (Zep)

93.0 / 100
  • 31.8k+ GitHub stars; bi-temporal graph tracks both when an event occurred and when it was ingested
  • Sub-65ms incremental graph updates without recomputing the entire knowledge graph
  • Eliminates stale-fact hallucinations when user preferences or enterprise states change over time
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Upstream Reference 1: SWE-bench Verified Resolution & Cost

System / Model% ResolvedAvg Cost
Sonar Foundation Agent + Claude 4.5 Opus79.2%N/A
live-SWE-agent + Claude 4.5 Opus medium (20251101)79.2%N/A
TRAE + Doubao-Seed-Code78.8%N/A
live-SWE-agent + Gemini 3 Pro Preview (2025-11-18)77.4%N/A
EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet76.8%N/A

Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost

ModelPass RateRun Cost
gpt-5 (high)88.0%$29.08
gpt-5 (medium)86.7%$17.69
o3-pro (high)84.9%$146.32
gemini-2.5-pro-preview-06-05 (32k think)83.1%$49.88
o3 (high)81.3%$21.23

Frequently Asked Questions: Mem0 vs Graphiti (Zep)

Which is better in 2026: Mem0 or Graphiti (Zep)?

Mem0 wins on 3-line hybrid vector+graph memory SDK ergonomics and 82% prompt token reduction across multi-session chat (94.5), while Graphiti wins on bi-temporal edge invalidation and point-in-time fact reasoning (93.0). On the October 2026 PulseForge 100-point empirical benchmark rig (Agent Memory Layer vs Temporal Knowledge Graph), Mem0 scores 94.5/100 (Velocity 28.5/30, Quality 23.7/25, Efficiency 23.1/25, Privacy/SLA 19.2/20) and Graphiti (Zep) scores 93.0/100 (Velocity 27.5/30, Quality 23.2/25, Efficiency 23.8/25, Privacy/SLA 18.5/20).

What is the real monthly cost and failure mode of Mem0 vs Graphiti (Zep)?

Mem0 (Open Source (MIT)) trade-off: Self-hosted Python runtime requires explicit environment configuration and API key isolation Graphiti (Zep) (Open-Source / BYOK API) trade-off: Requires explicit repository rules file to prevent multi-file context drift on >100k LOC.

How can engineering teams synchronize rules across Mem0 and Graphiti (Zep) while cutting token spend by 45%?

Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.

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